AI-assisted SaaS features for drafting, summarizing or classifying.
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AI inside the product
AI Development for Real Products
Build AI features that help users finish a real task. Connect models to your application, relevant context and existing workflows, with clear limits on what the system can do.
What this is for
Useful AI behavior inside working software, with review, fallback and access controls designed alongside the feature.
LLM integrationsContext & retrievalProduct workflows
Who it is for
For teams taking AI beyond a standalone demo.
- Founders building an AI-powered SaaS product.
- Small product teams adding AI to an existing application.
- Teams moving a promising model prototype into a user-facing workflow.
Problems it can solve
- A convincing demo that does not fit how users actually work.
- Outputs that lack relevant context or are hard to verify.
- Model calls disconnected from application permissions, failure states and cost limits.
What can be built
Useful model behavior inside real applications.
Start with the input and the result a user can verify. Retrieval can supply relevant context; validation and ordinary application logic decide what happens to the output.
Search and retrieval over approved information when context is needed.
Structured extraction with validation before data is saved.
Bounded assistants that propose actions for user approval.
Typical scope
Plan the context, controls and failure states.
Data access, expected behavior and integration complexity determine the build plan. A prototype review can help separate a useful feature from an uncertain research problem.
- Use-case and data-readiness review
- Model/API integration with server-side credentials
- Context retrieval and permission boundaries where needed
- Structured output validation and representative test cases
- User review, error handling and fallback behavior
- Deployment configuration and usage/cost visibility
Process
Define the task
Specify the input, useful output and decisions that must stay with a person. Check whether AI is needed at all.
Test the behavior
Try representative examples and edge cases. Agree what needs review and what an unacceptable result looks like.
Integrate
Connect the feature to the product UI, data permissions and backend, including validation and provider failures.
Release & learn
Review behavior in the target environment and agree how quality, latency and usage will inform the next iteration.
Relevant proof
See assisted behavior in a product workflow.
Keep exploring
Explore the product decisions around AI.
Separate model judgment from software rules.
Use AI where interpretation helps
Language-heavy tasks such as summarizing and drafting can benefit from models, provided users can check the result.
Keep rules in normal software
Permissions, billing calculations and fixed business rules belong in deterministic code. A model should not be the authority for these decisions.
Design for imperfect outputs
Make uncertainty visible. Validate formats, handle missing context and keep consequential actions behind explicit approval.
Questions about models, data and integration.
Do we need to train a custom model?
Not necessarily. A hosted model with suitable prompts, context and validation may be enough. Custom training should follow an identified need and a review of the available data.
Can AI be added to our existing application?
Yes, subject to reviewing its APIs, permissions and deployment setup. A bounded feature is often a clearer starting point than replacing the whole workflow.
What about private data and model errors?
Data access and provider terms need review before implementation. The design should limit information sent to a model and include validation, fallback behavior and human review for consequential outputs.
Next step
Define what the AI feature should actually do.
Bring a representative task, the available data and any existing prototype. The first discussion can establish where a model helps, what needs validation and which decisions should stay in code.